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Soft real-time applications require timely delivery of messages conforming tothe soft real-time constraints. Satisfying such requirements is a complex taskboth due to the volatile nature of distributed environments, as well as due tonumerous domain-specific factors that affect message latency. Prompt detectionof the root-cause of excessive message delay allows a distributed system toreact accordingly. This may significantly improve compliance with the requiredtimeliness constraints.  In this work, we present a novel approach for distributed performancemonitoring of soft-real time distributed systems. We propose to employ recentdistributed algorithms from the statistical signal processing and learningdomains, and to utilize them in a different context of online performancemonitoring and root-cause analysis, for pinpointing the reasons for violationof performance requirements. Our approach is general and can be used formonitoring of any distributed system, and is not limited to the soft real-timedomain.  We have implemented the proposed framework in TransFab, an IBM prototype ofsoft real-time messaging fabric. In addition to root-cause analysis, theframework includes facilities to resolve resource allocation problems, such asmemory and bandwidth deficiency. The experiments demonstrate that the systemcan identify and resolve latency problems in a timely fashion.

A Statistical Approach to Performance Monitoring in Soft Real-Time Distributed Systems